Klaus-Robert Müller et al. Big Data and Machine Learning

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1 Klaus-Robert Müller et al. Big Data and Machine Learning

2 Some Remarks Machine Learning small data (expensive!) big data big data in neuroscience: BCI et al. social media data physics & materials

3 Toward Brain Computer Interfacing Klaus-Robert Müller, Siamac Fazli, Jan Mehnert, Stefan Haufe, Frank Meinecke, Paul von Bünau, Franz Kiraly, Felix Biessmann, Sven Dähne, Johannes Höhne, Michael Tangermann, Carmen Vidaure, Gabriel Curio, Benjamin Blankertz et al.

4 Invasive BCI at it s best Remark: 24*1000* 3600*30000 ~ 2tb/day [From Schwartz]

5 Noninvasive Brain-Computer Interface DECODING

6 BCI for communcation

7 Brain Pong with BBCI Remark: 3*100* 3600*1000 ~ 1-2Gb/Experiment

8 BBCI paradigms Leitmotiv: let the machines learn - healthy subjects untrained for BCI A: training <10min: right/left hand imagined movements infer the respective brain acivities (ML & SP) B: online feedback session

9 Machine learning approach to BCI: infer prototypical pattern Inference by CSP Algorithm

10 The cerebral cocktail party problem use ICA/NGCA projections for artifact and noise removal feature extraction and selection [cf. Ziehe et al. 2000, Blanchard et al. 2006]

11 BBCI Set-up Artifact removal [cf. Müller et al. 2001, 2007, 2008, Dornhege et al. 2003, 2007, Blankertz et al. 2004, 2005, 2006, 2007, 2008]

12 Shifting distributions within experiment

13 20 Correlating apples and oranges [Biessmann et al. Neuroimage 2012, Machine Learning 2010]

14

15 Temporal Dynamics of Web Data

16 Motivation [Biessmann et al, 2012, and submitted]

17 Canonical Trend Analysis for Social Networks

18 Data Extraction

19 Data Extraction: Retweet Location

20 Mean Location of Reweeted News Articles

21 Downsampling of Geographic Information

22 Canonical Trend Model

23 Why projecting on canonical subspace Recent development: tkcca allows to optimally and nonlinearly correlate over time [Biessmann et al 2010]

24 Canonical Trend Analysis

25 Canonical Trend Analysis

26 Efficient Computation of Canonical Trends [Schölkopf, Smola & Müller 98, Boser, Gyon, Vapnik, 92]

27 Efficient Computation of Canonical Trends

28 Efficient Computation of Canonical Trends

29 Comparisons: Mean, PCA and Canonical Trends

30 Comparisons: Mean, PCA and Canonical Trends

31 Comparisons: Mean, PCA and Canonical Trends

32 Comparisons: Mean, PCA and Canonical Trends

33 Canonical Convolution

34 Spatiotemporal Analysis of Retweets of News

35 53 And now for something completely different [Montavon et al 13, Rupp et al 2012.]

36 IPAM 2011 Klaus-Robert Müller, Matthias Rupp Anatole von Lilienfeld and Alexandre Tkachenko et al

37 Machine Learning for chemical compound space Ansatz: instead of [from von Lilienfeld]

38 Machine Learning for chemical compound space Ansatz: Provide same information to ML as to SE: XYZ-file cast data similarly as in the SE: Unique and continuous in all of CCS Translationally, rotationally, permutationally invariant Symmetrical atoms contribute equally ``Coulomb'' Matrix [energy] fill up with zeros for smaller molecules diagonalize OR sort rows according to their norm measure distance between molecules: [from von Lilienfeld]

39 Coulomb representation of molecules M = 2.4 ii Z i M ij = R Z i i Z j R j M {Z 2, R 2 } {Z 1, R 1 } { Z 3, R 3 } {Z 4, R 4 }... M ij + phantom atoms {0,R 21 } {0,R 22 } {0,R 23 } Coulomb Matrix (Rupp12)

40 Kernel ridge regression Distances between M define Gaussian kernel matrix K Predict energy as sum over weighted Gaussians using weights that minimize error in training set Exact solution As many parameters as molecules + 2 global parameters, characteristic length-scale or kt of system (σ), and noise-level (λ) [from von Lilienfeld]

41 The data GDB-13 database of all organic molecules (within stability & synthetic constraints) of 13 heavy atoms or less: 0.9B compounds Blum & Reymond, JACS (2009) [from von Lilienfeld]

42 Results March 2012 Rupp et al., PRL 9.99 kcal/mol (kernels + eigenspectrum) December 2012 Montavon et al., NIPS 3.51 kcal/mol (deep Neural nets + Coulomb sets) More fun is yet to come... Prediction considered chemically accurate when MAE is below 1 kcal/mol Dataset available at

43 Conclusion Machine Learning is a versatile and ready to use tool for data analysis small data vs. big data fields of ML & Data Bases will hit a limit in near future time for a new marriage

44

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